Vincent Van Gogh
Portrait de l'artiste
1889
Introduction
Rationale
The primary catalyst for my interest in determining whether or not there is a ‘Correlation between the Rate of Depression in a Nation’s Population and a Nation’s Human Development Index Score’ was my participation in and later leadership of a non-profit organization dedicated to educating students about mental and physical health, the data behind it, and available resources to draw on for further information or aid. Through my position as a teacher of well researched medical information to my peers and later the president of the NPO branch, I have first-hand experiences of the commonness, and concomitantly, the severity of issues involving mental health which affect members of my educational community.
More broadly, however, it is difficult to ignore the constant stream of studies reported upon by the press which find that adolescents specifically, and societies of the world generally, are becoming more depressed, anxious, and challenged by mental health issues. The New York Times’ Matt Richtel delineated the findings of the Surgeon General of the United States’ 53 page report regarding a mental health crisis affecting American youth in 2020 (Richtel, 2021), while over a year before Geiger and Davis of the Pew Research Center noted the growing number of teenagers facing depression (Geiger & Davis, 2019). More recently, Daniel Santomauro and colleagues of the Lancet conducted a metanalysis of the data of 204 countries, determining that the COVID-19 pandemic has caused an increase in the prevalence and burden of depressive and anxiety disorders (Santomauro et al, 2021). To say the least, it is evident that mental health is a preeminent issue in the world and ought to be treated with seriousness deserving of such.
In combating depression and mental illness in general, understanding the nature of their propagation is key. The concentration of the issue in developed countries is especially of note, and leads to my inquiry into whether scores on the Human Development Index (explained in detail below) have a connection to rates of depression and what that can tell us about understanding, preventing, and treating depressive and anxiety disorders around the world.
Research Background Information
The Human Development Index (HDI) is a commonly used measure of the socioeconomic development of a given nation. It is acquired through a combination of the analyses of three separate indices – 1) life expectancy at birth, 2) expected years of schooling and mean years of schooling, and 3) gross national income per capita. Each of these indices is scored from 0.0-1.0, with 0.0 being the least and 1.0 being the most – the geometric mean of the scores is then taken, and the resulting HDI score (also between 0.0 and 1.0) is produced. Nations which have a score higher than 0.788 are considered developed, while those which have a score lower than 0.788 are considered developing (Thompson, 2017).
Depression is, according to the American Psychiatric Association, a “common and serious medial illness that negatively affects how you feel, the way you think and how you act…Depression causes feelings of sadness and/or a loss of interest in activities you once enjoyed. It can lead to a variety of emotional and physical problems and can decrease your ability to function at work and at home.” (APA, 2020). Importantly and unfortunately, depression can be a key cause in thoughts about death and suicide and eventual action on said thoughts. The World Health Organization found 3 that approximately 280,000,000 people suffer from depression globally and that 700,000 people commit suicide each year (WHO, 2021). The universal relevance of further research into this topic is, therefore, unquestionable.
Hypotheses and Methodology
Hypotheses
In consideration of the fact that the investigation is that of ‘the Correlation between the Rate of Depression in a Nation’s Population and a Nation’s Human Development Index Score’, the hypotheses are as such:
Table 1: Established Hypotheses
The reasoning behind the pursuit of the possibility that nations with higher HDIs have higher rates of depression is that, through the cursory observations outlined via the sources of the Rationale and Background Information subsections, it appears that the recent increases in depression and anxiety, whilst being globally increased due to the pandemic, have had an outsized effect on the young populations of developed countries. Research into the connection between increased wealth (an element of HDI) and depression, such as that of Suniya S. Luthar in the Journal of Child Development, has yielded undeniable positive results for the existence of such a connection upon which the rationale for the alternative hypothesis of this investigation is founded (Luthar, 2003).
Methodology
With the aim of exploring the topic at hand in some depth and thereby providing not only a confirmation of the existence of the correlation but also an attempt at explaining why it exists, if it does, the investigation was split into two bodies – A and B.
Body A was devoted to investigating the main aspect of the study; ‘the Correlation between the Rate of Depression in a Nation’s Population and a Nation’s Human Development Index Score’.
Body B was devoted to investigating further the results of Body A. In the case of the rejection of the null hypothesis, Body B would serve to find out why countries with higher HDIs have higher rates of depression. In the case of the acceptance of the null hypothesis, Body B would serve to find out why countries’ rates of depression do not vary by HDI score.
Body A: Investigating the Correlation between the Rate of Depression in a Nation’s Population and a Nation’s Human Development Index Score
1. Raw data was collected from the World Health Organization (WHO) for number of individuals with depressive disorders by country and from the United Nations Development Programme for HDI scores by country.
2. The WHO data was processed to be in percentage form so as to equalize the weight of the data for each country regardless of population size.
3. The order of the two datasets of percent of population with depression and HDI by country were randomized and then culled to 100 countries to produce more manageable datasets.
4. The datasets were combined into a single table, sorted alphabetically.
5. The table was utilized to produce a scatterplot graph visualizing the correlation and directionality between percent of population with depression and HDI score.
6. The combined was input into a battery of equations to determine specific values regarding correlation: Mean, Standard Deviation, Covariance, Pearson’s Correlation Coefficient, and a Regression Line Equation.
Body B: Investigating Possible Causes of the Relationship Established by Body A.
1. Research regarding causes of depression was analyzed and searched for factors connected to HDI.
2. Two possible factors: Substance Abuse, as split into drug abuse and alcohol abuse.
3. Raw data used to indicate the prevalence of the above factors was collected for the randomly selected nations of the first dataset from the WHO and the US Institute of Health Metrics and Evaluations.
4. The raw data was processed for age-standardization where relevant, and inducted into a multi-variable dataset table.
5. The two factors were analyzed using Pearson’s Correlation Coefficient – firstly in correlation with HDI scores and secondly in correlation with percent of population with depression.
6. Graphs of these relationships were created.
7. The resulting correlation values between the three 3 variable groups (1: HDI, 2: Depression, and 3: Substance Abuse) was used to consider the validity of the two noted factors as causes of depression
Body of Investigation A
Combined Data Table
Table 2 Pt.1: Combined Dataset of HDI Scores and Percent of Population with Depression by Country, Sorted Alphabetically
Table 3 P.2: Combined Dataset of HDI Scores and Percent of Population with Depression by Country, Sorted Alphabetically
Graph 1: Scatterplot Showing Correlation between HDI Score and Percentage of Population with Depression
Application of Analytical Equations
Mean of X and Y
The mean of x, 0.7347 shows the average national HDI in the sample. When contextualized with the maximum value of x, 0.96 in Norway, and the minimum value of x, 0.40 in the Central African Republic, the mean is shown to be closer to the maximum value, demonstrating the extremity of low HDI scores close to 0.40.
The mean of y, 4.487, shows the average percentage of a sample population that is depressed. When contextualized with the maximum value of y, 6.3% in Ukraine, and the minimum value of y, 3% in Timor-Leste, the mean is shown to be a similar distance from both the maximum and minimum values, demonstrating that both values have a similar distance from the mean.
Standard Deviation of X and Y
The standard deviation of x, 0.1473091986, represents the average distance of any given x value from the mean. In this case, the low standard deviation shows close conformity to the mean and relatively high reliability of the data sample.
The standard deviation of y, 0.7050751733, represents the average distance of any given y value from the mean. In this case, though the standard deviation of y is greater than of x, it is still below 1 standard deviation, showing conformity to the mean and relatively high reliability of the data sample.
Pearson’s Correlation Coefficient
When judged on Mindrila and Balentyne’s scale where a Pearson’s Correlation Coefficient value of 0.0<r<0.3 is very weak, 0.3<r<0.5 is weak, 0.5<r<0.7 is moderate, and 0.7<r<1.0 is strong, it is clear that the correlation between HDI score and percent of population with depression is moderate and positive. That is to say, the higher the HDI, the higher the rates of depression.
Covariance
A covariance value of 0.06138049 shows the directionality of the linear relationship between the two variables, in this case indicating that the direction is positive and linear. Unlike Pearson’s Correlation Coefficient, the lack of standardization of units means that the strength of the relationship is nonassessable, however, the positive direction remains clear, indicating, alongside Pearson’s r value, that the relationship between HDI and rates of depression is moderate, positive, and progressive in a linear manner.
Equation of the Regression Line
The equation of the regression line shown above is used to show how the dependent variable (y) responds to changes in the independent variable (x) and allow for prediction of data via extrapolation. In this case, it is indicated that for every increase in x, the national HDI score, there will be an increase in y, the percent of the population with depression, reaffirming that as HDI increases, so too do rates of depression.
Being that HDI has a ceiling of possibility for its value – that the score can never increase more than 1.0 – the extrapolative power of the equation of the regression line, although existent, is realistically unusable beyond x=1, as no nation would ever reach an HDI beyond 1.0. Notwithstanding, rates of depression could certainly go above the extrapolated prediction, as, theoretically, 100% of a given population could be depressed.
Body of Investigation B
Research
In attempting to understand the above-established relationship between increases in HDI and increases in depression, it is helpful to analyze the components of a nation’s HDI score in more detail.
As outlined in the Background Information subsection, HDI is composed of three separate indices – observing whether the factors measured by those indices individually have an effect on depression serves to provide a more detailed and specific comprehension of why there is a relationship between HDI and depression. The following will be a researched exploration of whether any of the sub-indexes of HDI have any relevance to the observed relationship.
Life Expectancy: Haomiao Jia et al find, in Social Psychiatry and Psychiatric Epidemiology, that rather than a longer life increasing the chances of depression, depression as a disorder shortens quality-adjusted life expectancy. Therefore, the higher life expectancies concomitant with higher HDIs are not a cause of depression (Jia et al, 2015).
Education: A large collection of repeatedly reaffirmed research, ranging from Mirowsky and Ross, 2003; to Pearlin et al, 2005; to McFarland and Wagner, 2015; shows that higher levels of education are consistently associated with better mental health. Though the exact causality is unclear, it remains evident that the higher levels of education concomitant with higher HDIs are not a cause of depression.
Gross National Income per Capita / Wealth: Unlike the two factors above, wealth is a very broad factor, which can be split into many subsections. As an inclusion criteria, the medical causes of depression must be identified, as they usefully are by the National Health Service of the UK. They are: 1) Stressful Events, 2) Personality, 3) Family History, 4) Giving Birth, 5) Loneliness, 6) Alcohol and Drugs, and 7) Illness (NHS, 2019). The first three factors and 7) Illness can be disregarded, as they can be taken as broadly similar across global populations, whilst giving birth is more common in less wealthy countries. This leaves 5) Loneliness and 6) Alcohol and Drugs as the two factors connected to increases in wealth that ought to be considered. 6) Alcohol and Drugs is a factor which can be checked by an analysis of the correlation of alcohol and drug abuse with HDI. 5) Loneliness, on the other hand, is not an easily measurable statistic, and as such, will be left out of the investigation for the sake of clarity, though should not be forgotten as a contributing factor to depressive and anxiety disorders.
Table 4: Multi-variable Dataset Table Containing the Information of Table 2 and Percentage of Population with Drug Abuse Disorders and Percentage of Population with Alcohol Abuse Disorders by Country, Sorted Alphabetically
The combined data in the table above was used to create the graphs below and to calculate the respective Pearson's Correlation Coefficients. The graphs are used to plot the statistical relationship between Depression Pervalance and Alcohol and Drug Abuse Disorders, respectively, and HDI Score and Alcohol and Drug Abuse Disorders, respectively.
Graph 2: Scatterplot Showing Correlation (r=0.460) between Percentage of Population with Depression and Percentage of Population with Alcohol Abuse Disorders
Graph 3: Scatterplot Showing Correlation (r=0.520) between Percentage of Population with Depression and Percentage of Population with Drug Abuse Disorders
Graph 4: Scatterplot Showing Correlation (r=0.392) between National HDI Score and Percentage of Population with Alcohol Abuse Disorders
Graph 5: Scatterplot Showing Correlation (r=0.677) between National HDI Score and Percentage of Population with Drug Abuse Disorders
The above data was acquired through the application of the Pearson’s Correlation Coefficient equation, as shown in Body of Investigation A.
Conclusion
Observational Descriptions
With all the factors and figures laid out above, the following conclusions can be drawn regarding the investigation of ‘the Correlation between the Rate of Depression in a Nation’s Population and a Nation’s Human Development Index Score:
With regard to Body of Investigation A, it was made clear that there is a definitive correlation between the two main variables under investigation. The correlation was established to be a positive and linear relationship with a moderate strength based on Pearson’s Correlation Coefficient value of r=5.91, indicating that the null hypothesis must be rejected outright. With further regard to Body of Investigation B, certain specific elements of the above relationship were revealed. Each Pearson’s Correlation Coefficient score of Body B was at least weak (r=0.392 and r=0.460), moderate (r=0.520), and at most (r=0.677) nearly strong, therefore showing the three-way, interconnected relationship between HDI score, percentage of population with depression, and percentage of population with substance abuse (alcohol and drug related). That is to say, as national HDI – wealth, specifically – increases, so too does depression, which, through a combination of statistical analysis and previous medical research, can be concluded to in part originate from higher rates of substance abuse (drugs moreso than alcohol) in nations with higher HDI scores.
Insights & Recommendations
From the main conclusion that HDI indeed does have a direct statistical relationship with depression are derived several important insights which those with influence over policies and laws, be they national or local, ought to take note of. Although difficult to quantify past self-reported surveys and not included in this report, loneliness, as noted above, is an instigator of depression which the increased use of technology, the decrease of population density and walkability, especially in the U.S., and most recently the COVID-19 pandemic have exacerbated severely. Therefore, the provision of greater community spaces and resources, both physical and virtual, and the slow shift to city planning focused on curbing sprawl, increasing urbanization and "Third Places", and ensuring walkability are some of the keys to tackling. Concerning the insights specific to this paper, substance abuse is evident to increase with HDI (though alcohol is prevalent among nations with lower HDIs as well) and, as such, needs to be an area of greater focus for policy-makers. Substance abuse is often portrayed as a personal failure on the part of the individual, but this perspective often fails to analyze the systems, or rather the lack thereof, which lead to substance abuse not only in individuals but vast populations. A lack of access to personal development opportunities in education and vocation, as well as the previously noted destruction of community spaces and resources, are demonstrable systematic failures which directly lead to depressive and anxiety disorders and to substance abuse, which itself leads to further depressive and anxiety disorders along with serious physical health issues and concomitant financial. As is generally the case, it is the duty of law and policy makers to rework and create systems based on sound principles of institutional economics, such that the systems they build can provide a positive service to people whilst themselves remaining functional, adaptable, and transparent.
Limitations
With regard to limitations, it must be noted that, though highly valuable, the establishment of the statistical connection between increased HDI, increased substance abuse, and increased depression must be followed up by further research which reveals what factors other than substance abuse increase depression rates in wealthier countries. As noted, loneliness forms a key part in development of depressive disorders, and finding a way to measure it would prove essential in future research into the topic. A possible place to start would be rates of internet use and addiction – which increase as HDI increases – and their effects on the psyche. Research by Banjanin et al in 2015 and Cheng and Li in 2014 has shown a link between internet usage and mental health issues; further studies and data collection on the matter could yield promising results.
Lastly, also on the note of limitations, it must be acknowledged that, as with any study of international data, the methods of collection are naturally hampered by the resources data collectors have at hand. Those in wealthier countries will likely have greater funding and communicational resources with which to gather information, and thus, underreporting of statistics in less fortunate nations remains a chronic issue in research.
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Really insightful! The amount of detail gone into your calculations is quite impressive.